Staff View
Railroad trespassing detection and analysis using video analytics

Descriptive

TitleInfo
Title
Railroad trespassing detection and analysis using video analytics
Name (type = personal)
NamePart (type = family)
Zaman
NamePart (type = given)
Asim F.
NamePart (type = date)
1992-
DisplayForm
Asim F. Zaman
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Liu
NamePart (type = given)
Xiang
DisplayForm
Xiang Liu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Gong
NamePart (type = given)
Jie
DisplayForm
Jie Gong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Jin
NamePart (type = given)
Jing
DisplayForm
Jing Jin
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2018
DateOther (qualifier = exact); (type = degree)
2018-10
CopyrightDate (encoding = w3cdtf)
2018
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Trespassing is the leading cause of rail-related deaths and has been on the rise for the past ten years. Detection of trespassing of railroad tracks is critical to understand and prevent trespassing fatalities. The volume of video data in the railroad industry has increased significantly in recent years. Surveillance cameras are situated on nearly every part of the railroad system such as inside the cab, along the track, at grade crossings, and in stations. These camera systems are manually monitored; either live or subsequently reviewed in an archive, which requires an immense amount of labor. To make the video analysis much less labor-intensive, this thesis develops two frameworks for utilizing Artificial Intelligence (AI) technologies for the extraction of useful information from these big video datasets.
The first framework has been implemented on video data from one grade crossing in New Jersey. The AI algorithm can automatically detect unsafe trespassing of railroad tracks. To date, the AI algorithm has analyzed hours of video data and correctly detected all trespassing events. The algorithm was presented to industry professionals and useful feedback was gathered suggesting several improvements to meet the needs of the railroad industry. This feedback led to the development of the second framework with new capabilities, and an expanded scope of video data reviewed.
The second framework was implemented on three railroad video live streams, a grade crossing and two non-grade crossings, in the United States. This AI algorithm automatically detects trespassing events, differentiates between the type of violator (car, motorcycle, truck, pedestrian etc.) and sends an alert text message to a designated destination with important information including a video clip of the trespassing event. In this study, the AI has analyzed hours of live footage with no false positives or missed detections.
This thesis indicates the promise of using AI for automated analysis of railroad big video data, thereby supporting data-driven railroad safety research. This thesis, and its sequent studies, aim to provide the railroad industry with next-generation big data analysis methods and tools for quickly and reliably processing large volumes of video data to better understand human factors in railroad safety research.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
Subject (authority = ETD-LCSH)
Topic
Railroads--Safety measures
Subject (authority = ETD-LCSH)
Topic
Data logging
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9317
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (76 pages : illustrations)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Asim F. Zaman
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-mc8m-nv44
Genre (authority = ExL-Esploro)
ETD graduate
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Zaman
GivenName
Asim
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-02 12:17:46
AssociatedEntity
Name
Asim Zaman
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.7
ApplicationName
Microsoft® Word for Office 365
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2018-10-02T15:54:18
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2018-10-02T15:54:18
Back to the top
Version 8.5.3
Rutgers University Libraries - Copyright ©2024